federated learning

Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation

Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.

Federated Learning with Differential Privacy: Enhancing Privacy in Decentralized Model Training

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framework to provide an enhanced level of privacy protection. We introduce a novel approach where noise is injected into model parameters during the training process, adhering to the principles of DP. This mechanism mitigates the risk of information leakage from individual user data while maintaining model accuracy. We formally define the proposed algorithm and analyze its privacy-utility trade-off. The results demonstrate that the incorporation of DP significantly strengthens the privacy guarantees of FL, offering a robust solution for privacy-sensitive applications. Our work contributes to the growing body of research focused on secure and privacy-preserving FL techniques.

**Decentralized Federated Learning with Byzantine Fault Tolerance using Graph Protocols**

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Distributed Bayesian Inference with Federated Learning and Differential Privacy

This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs). This framework addresses the limitations of traditional federated learning, particularly when dealing with graph-structured data, where raw data sharing poses significant privacy concerns. The system avoids direct data exchange by allowing devices to collaboratively learn through the exchange of model updates, preserving data privacy while enabling effective knowledge transfer. The proposed FedGNN architecture consists of local graphs and locally trained GNNs, synchronized via a novel aggregation mechanism. We demonstrate the feasibility and effectiveness of this approach through a theoretical analysis and outline the key components required for implementation. The system is designed to be scalable and adaptable to various decentralized network topologies. This work represents a significant step towards privacy-preserving collaborative learning in complex, graph-based environments.

Machine Learning-Based Spectrum Forecasting for Ultra-Dense IoT Environments

The reach of Internet of Things (IoT) devices in cross domain applications has recently resulted in a significant rise in the burden on wireless communication systems. The modern wireless communication systems are under stress due to fast deployment of the Internet of Things’ (IoT) in homes, businesses, and smart cities. It becomes more challenging to balance the bandwidth in these ultra-dense environments. Here many heterogeneous devices are involved and need a finite amount of radio spectrum. It becomes difficult for conventional spectrum assignment and sensing techniques to manage due to unpredictable traffic volumes, varying interference, and quickly changing channel conditions. Hence, there is the necessity for future systems to find a reactive mechanism and implement predictive strategies that can predict the changes in the spectrum in coming times. Machine learning has become a promising technology facilitator in this context. The predictive abilities of ML make it possible to have a smarter transmission scheduling, enhanced interference avoidance, and more effective use of limited spectrum resources. This chapter showcases a review and assessment of ML-based spectrum forecasting methods designed for dense IoT environments and setups. It reviews a wide range of learning techniques, like deep learning architectures, supervised and unsupervised methods, and reinforcement learning models, and discusses on how it can be deployed for significant tasks like channel occupancy prediction, interference mitigation, and enabling dynamic access decisions. This chapter also includes the review and assessment of emerging system architectures that can reduce latency, save device energy, and ensure privacy in distributed IoT networks, like edge-assisted and federated learning. The issues like how ML-enabled forecasting improves spectrum efficiency, reduces collision rates, and helps in maintaining quality of service (QoS) in large-scale IoT deployments through comparative analysis and case-driven discussion is also discussed in this chapter. The chapter finally summarizes new research directions that impacts the development of spectrum-intelligent IoT and 6G communication systems in future.

Distributed Bayesian Inference with Federated Learning and Differential Privacy

This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.

Decentralized Federated Reinforcement Learning with Multi-Agent Credit Assignment

This paper presents a novel approach to decentralized federated reinforcement learning (DFRL) that tackles the critical challenge of multi-agent credit assignment. Traditional multi-agent reinforcement learning (MARL) struggles to scale effectively in decentralized settings due to the difficulty in determining which agents are responsible for the overall reward. Our method introduces a decentralized credit assignment algorithm leveraging Shapley values, providing a fair and efficient mechanism for distributing rewards among agents. Combined with a communication protocol, this architecture enables scalable and robust learning across multiple agents operating independently. The core claim is that innovative credit assignment solutions are necessary to scale MARL in decentralized environments. This work offers a foundational framework for developing more practical and effective DFRL systems.

Machine Learning - Based Classification Framework for Intelligent 5G Resource Allocation and Network Optimization

Communication-Efficient Federated Frank-Wolfe Algorithm

Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation

Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.

FedBound a boundary aware optimization strategy for federated medical image segmentation under non IID data

Abstract Medical image segmentation is essential for computer-aided diagnosis and treatment planning. Privacy constraints impede centralized training using medical images from diverse healthcare organizations. Federated learning (FL) is developed to facilitate collaborative model training without sharing patient information. However, the statistical heterogeneity of data (non-IID distribution) among clients adversely impacts the segmentation task, particularly the quality of segmentation at object borders, which remains insufficiently explored. This paper presents a systematic analysis of boundary-sensitive medical image segmentation under heterogeneous federated learning conditions. We propose FedBound , a lightweight boundary-aware optimization technique that emphasizes contour areas during local training without increasing communication overhead. Additionally, we examine the impact of multiscale feature representations using an ASPP-based federated framework termed FedASPP . Experiments were conducted on the ISIC 2018 dataset for skin lesion segmentation, employing a Dirichlet non-IID distribution across 100 federated clients. The findings indicate that FedBound enhances boundary quality, reduces the HD95 score, and maintains high Dice and IoU coefficients across various segmentation architectures. Furthermore, FedBound reduces performance variability among clients, demonstrating improved stability in heterogeneous federated environments.